Image cropping

ABSTRACT

Briefly, embodiments disclosed herein relate to image cropping, such as for digital images, for example.

RELATED APPLICATIONS

This application is a continuation and claims priority of U.S. patentapplication Ser. No. 14/588,213, entitled “Image Cropping,” by DaozhengChen et al, filed on Dec. 31, 2014, which is incorporated herein byreference in its entirety and for all purposes.

BACKGROUND Field

Subject matter disclosed herein may relate to image cropping.

Information

With networks, such as the Internet, gaining popularity, and with a vastmultitude of content, such as pages, other electronic documents, othermedia content and/or applications (hereinafter ‘digital content’),becoming available to users, such as via the World Wide Web (hereinafter‘Web’), it may be desirable to provide more efficient and/or morestreamlined approaches to gather, organize and/or display content, suchas digital content, that may be desired by and/or useful to a user, forexample. Internet-type business entities, such as Yahoo!, for example,may provide a wide range of content, such as digital content, that maybe made available to users, such as via the Web. Internet-type businessentities, such as Yahoo!, for example, may provide image content toclients, such as client computing devices, for example. In somecircumstances, challenges may be faced in providing image content, suchas digital image content, to client computing devices, such as fordisplay within a Web page at a client computing device, for example.

BRIEF DESCRIPTION OF THE DRAWINGS

Claimed subject matter is particularly pointed out and distinctlyclaimed in the concluding portion of the specification. However, both asto organization and/or method of operation, together with objects,features, and/or advantages thereof, it may best be understood byreference to the following detailed description if read with theaccompanying drawings in which:

FIG. 1 is a schematic diagram illustrating an example networkedcomputing system in accordance with an embodiment.

FIG. 2 is an illustration of an example process for image cropping,according to an embodiment.

FIG. 3 is a schematic diagram illustrating another example process forimage cropping, according to an embodiment.

FIG. 4 is an illustration depicting an example selection of a region ofan example image, in accordance with an embodiment.

FIG. 5 is an illustration depicting an additional example selection of aregion of another example image, in accordance with an embodiment.

FIG. 6 is an illustration depicting another example selection of aregion of an additional example image, in accordance with an embodiment.

FIG. 7 is an illustration depicting another additional example selectionof a region of another additional example image, in accordance with anembodiment.

FIG. 8 is an illustration depicting an example face-type saliency mapfor an example image, in accordance with an embodiment.

FIG. 9 is an illustration depicting an example position-type saliencymap, in accordance with an embodiment.

FIG. 10 is a schematic diagram illustrating an example process forgenerating an example composite saliency map, according to anembodiment.

FIG. 11 is an illustration depicting an example selection of a croppingwindow for a region of an example image, in accordance with anembodiment.

FIG. 12 is a schematic diagram illustrating an example computing devicein accordance with an embodiment.

Reference is made in the following detailed description to accompanyingdrawings, which form a part hereof, wherein like numerals may designatelike parts throughout to indicate corresponding and/or analogouscomponents. It will be appreciated that components illustrated in thefigures have not necessarily been drawn to scale, such as for simplicityand/or clarity of illustration. For example, dimensions of somecomponents may be exaggerated relative to other components. Further, itis to be understood that other embodiments may be utilized. Furthermore,structural and/or other changes may be made without departing fromclaimed subject matter. It should also be noted that directions and/orreferences, for example, up, down, top, bottom, and so on, may be usedto facilitate discussion of drawings and/or are not intended to restrictapplication of claimed subject matter. Therefore, the following detaileddescription is not to be taken to limit claimed subject matter and/orequivalents.

DETAILED DESCRIPTION

References throughout this specification to one implementation, animplementation, one embodiment, an embodiment and/or the like means thata particular feature, structure, and/or characteristic described inconnection with a particular implementation and/or embodiment isincluded in at least one implementation and/or embodiment of claimedsubject matter. Thus, appearances of such phrases, for example, invarious places throughout this specification are not necessarilyintended to refer to the same implementation or to any one particularimplementation described. Furthermore, it is to be understood thatparticular features, structures, and/or characteristics described arecapable of being combined in various ways in one or more implementationsand, therefore, are within intended claim scope, for example. Ingeneral, of course, these and other issues vary with context. Therefore,particular context of description and/or usage provides helpful guidanceregarding inferences to be drawn.

With advances in technology, it has become more typical to employdistributed computing approaches in which portions of a problem, such assignal processing of signal samples, for example, may be allocated amongcomputing devices, including one or more clients and/or one or moreservers, via a computing and/or communications network, for example. Anetwork may comprise two or more network devices and/or may couplenetwork devices so that signal communications, such as in the form ofsignal packets and/or frames (e.g., comprising one or more signalsamples), for example, may be exchanged, such as between a server and aclient device and/or other types of devices, including between wirelessdevices coupled via a wireless network, for example.

In this context, the term network device refers to any device capable ofcommunicating via and/or as part of a network and may comprise acomputing device. While network devices may be capable of sending and/orreceiving signals (e.g., signal packets and/or frames), such as via awired and/or wireless network, they may also be capable of performingarithmetic and/or logic operations, processing and/or storing signals(e.g., signal samples), such as in memory as physical memory states,and/or may, for example, operate as a server in various embodiments.Network devices capable of operating as a server, or otherwise, mayinclude, as examples, dedicated rack-mounted servers, desktop computers,laptop computers, set top boxes, tablets, netbooks, smart phones,wearable devices, integrated devices combining two or more features ofthe foregoing devices, the like or any combination thereof. Asmentioned, signal packets and/or frames, for example, may be exchanged,such as between a server and a client device and/or other types ofnetwork devices, including between wireless devices coupled via awireless network, for example. It is noted that the terms, server,server device, server computing device, server computing platform and/orsimilar terms are used interchangeably. Similarly, the terms client,client device, client computing device, client computing platform and/orsimilar terms are also used interchangeably. While in some instances,for ease of description, these terms may be used in the singular, suchas by referring to a “client device” or a “server device,” thedescription is intended to encompass one or more client devices and/orone or more server devices, as appropriate. Along similar lines,references to a “database” are understood to mean, one or more databasesand/or portions thereof, as appropriate.

It should be understood that for ease of description a network device(also referred to as a networking device) may be embodied and/ordescribed in terms of a computing device. However, it should further beunderstood that this description should in no way be construed thatclaimed subject matter is limited to one embodiment, such as a computingdevice and/or a network device, and, instead, may be embodied as avariety of devices or combinations thereof, including, for example, oneor more illustrative examples.

Likewise, in this context, the terms “coupled”, “connected,” and/orsimilar terms are used generically. It should be understood that theseterms are not intended as synonyms. Rather, “connected” is usedgenerically to indicate that two or more components, for example, are indirect physical, including electrical, contact; while, “coupled” is usedgenerically to mean that two or more components are potentially indirect physical, including electrical, contact; however, “coupled” isalso used generically to also mean that two or more components are notnecessarily in direct contact, but nonetheless are able to co-operateand/or interact. The term coupled is also understood generically to meanindirectly connected, for example, in an appropriate context.

The terms, “and”, “or”, “and/or” and/or similar terms, as used herein,include a variety of meanings that also are expected to depend at leastin part upon the particular context in which such terms are used.Typically, “or” if used to associate a list, such as A, B or C, isintended to mean A, B, and C, here used in the inclusive sense, as wellas A, B or C, here used in the exclusive sense. In addition, the term“one or more” and/or similar terms is used to describe any feature,structure, and/or characteristic in the singular and/or is also used todescribe a plurality and/or some other combination of features,structures and/or characteristics. Likewise, the term “based on” and/orsimilar terms are understood as not necessarily intending to convey anexclusive set of factors, but to allow for existence of additionalfactors not necessarily expressly described. Of course, for all of theforegoing, particular context of description and/or usage provideshelpful guidance regarding inferences to be drawn. It should be notedthat the following description merely provides one or more illustrativeexamples and claimed subject matter is not limited to these one or moreillustrative examples; however, again, particular context of descriptionand/or usage provides helpful guidance regarding inferences to be drawn.

A network may also include now known, and/or to be later developedarrangements, derivatives, and/or improvements, including, for example,past, present and/or future mass storage, such as network attachedstorage (NAS), a storage area network (SAN), and/or other forms ofcomputing and/or device readable media, for example. A network mayinclude a portion of the Internet, one or more local area networks(LANs), one or more wide area networks (WANs), wire-line typeconnections, wireless type connections, other connections, or anycombination thereof. Thus, a network may be worldwide in scope and/orextent. Likewise, sub-networks, such as may employ differingarchitectures and/or may be compliant and/or compatible with differingprotocols, such as computing and/or communication protocols (e.g.,network protocols), may interoperate within a larger network. In thiscontext, the term sub-network and/or similar terms, if used, forexample, with respect to a network, refers to the network and/or a partthereof. Sub-networks may also comprise links, such as physical links,connecting and/or coupling nodes so as to be capable to transmit signalpackets and/or frames between devices of particular nodes includingwired links, wireless links, or combinations thereof. Various types ofdevices, such as network devices and/or computing devices, may be madeavailable so that device interoperability is enabled and/or, in at leastsome instances, may be transparent to the devices. In this context, theterm transparent refers to devices, such as network devices and/orcomputing devices, communicating via a network in which the devices areable to communicate via intermediate devices of a node, but without thecommunicating devices necessarily specifying one or more intermediatedevices of one or more nodes and/or may include communicating as ifintermediate devices of intermediate nodes are not necessarily involvedin communication transmissions. For example, a router may provide a linkand/or connection between otherwise separate and/or independent LANs. Inthis context, a private network refers to a particular, limited set ofnetwork devices able to communicate with other network devices in theparticular, limited set, such as via signal packet and/or frametransmissions, for example, without a need for re-routing and/orredirecting transmissions. A private network may comprise a stand-alonenetwork; however, a private network may also comprise a subset of alarger network, such as, for example, without limitation, all or aportion of the Internet. Thus, for example, a private network “in thecloud” may refer to a private network that comprises a subset of theInternet, for example. Although signal packet and/or frame transmissionsmay employ intermediate devices of intermediate nodes to exchange signalpacket and/or frame transmissions, those intermediate devices may notnecessarily be included in the private network by not being a source ordestination for one or more signal packet and/or frame transmissions,for example. It is understood in this context that a private network mayprovide outgoing network communications to devices not in the privatenetwork, but such devices outside the private network may notnecessarily be able to direct inbound network communications to devicesincluded in the private network.

The Internet refers to a decentralized global network of interoperablenetworks that comply with the Internet Protocol (IP). It is noted thatthere are several versions of the Internet Protocol. Here, the termInternet Protocol, IP, and/or similar terms, is intended to refer to anyversion, now known and/or later developed of the Internet Protocol. TheInternet includes local area networks (LANs), wide area networks (WANs),wireless networks, and/or long haul public networks that, for example,may allow signal packets and/or frames to be communicated between LANs.The term World Wide Web (WWW or Web) and/or similar terms may also beused, although it refers to a part of the Internet that complies withthe Hypertext Transfer Protocol (HTTP). For example, network devices mayengage in an HTTP session through an exchange of appropriatelycompatible and/or compliant signal packets and/or frames. It is notedthat there are several versions of the Hypertext Transfer Protocol.Here, the term Hypertext Transfer Protocol, HTTP, and/or similar termsis intended to refer to any version, now known and/or later developed.It is likewise noted that in various places in this documentsubstitution of the term Internet with the term World Wide Web (‘Web’)may be made without a significant departure in meaning and may,therefore, not be inappropriate in that the statement would remaincorrect with such a substitution.

Although claimed subject matter is not in particular limited in scope tothe Internet and/or to the Web; nonetheless, the Internet and/or the Webmay without limitation provide a useful example of an embodiment atleast for purposes of illustration. As indicated, the Internet and/orthe Web may comprise a worldwide system of interoperable networks,including interoperable devices within those networks. The Internetand/or Web has evolved to a public, self-sustaining facility that may beaccessible to tens of millions of people or more worldwide. Also, in anembodiment, and as mentioned above, the terms “WWW” and/or “Web” referto a part of the Internet that complies with the Hypertext TransferProtocol. The Internet and/or the Web, therefore, in this context, maycomprise an service that organizes stored content, such as, for example,text, images, video, etc., through the use of hypermedia, for example. AHyperText Markup Language (“HTML”), for example, may be utilized tospecify content and/or to specify a format for hypermedia type content,such as in the form of a file and/or an “electronic document,” such as aWeb page, for example. An Extensible Markup Language (“XML”) may also beutilized to specify content and/or format of hypermedia type content,such as in the form of a file or an “electronic document,” such as a Webpage, in an embodiment. Of course, HTML and/or XML are merely examplelanguages provided as illustrations. Furthermore, HTML and/or XML(and/or similar terms) is intended to refer to any version, now knownand/or later developed of these languages. Likewise, claimed subjectmatter is not intended to be limited to examples provided asillustrations, of course.

As used herein, the term “Web site” and/or similar terms refer to acollection of related Web pages. Also as used herein, “Web page” and/orsimilar terms refer to any electronic file and/or electronic document,such as may be accessible via a network, including by specifying a URLfor accessibility via the Web, in an example embodiment. As alluded toabove, in one or more embodiments, a Web page may comprise content codedusing one or more languages, such as, for example, markup languages,including HTML and/or XML, although claimed subject matter is notlimited in scope in this respect. Also, in one or more embodiments,application developers may write code in the form of JavaScript, forexample, to provide content to populate one or more templates, such asfor an application. The term ‘JavaScript’ and/or similar terms areintended to refer to any now known and/or later developed version ofthis programming language. However, JavaScript is merely an exampleprogramming language. As was mentioned, claimed subject matter is notintended to be limited to examples and/or illustrations.

As used herein, the terms “entry”, “electronic entry”, “document”,“electronic document”, “content”, “digital content”, “item”, and/orsimilar terms are meant to refer to signals and/or states in a physicalformat, such as a digital signal and/or digital state format, e.g., thatmay be perceived by a user if displayed, played and/or otherwiseexecuted by a device, such as a digital device, including, for example,a computing device, but otherwise might not necessarily be perceivableby humans (e.g., in a digital format). Likewise, in this context,content (e.g., digital content) provided to a user in a form so that theuser is able to perceive the underlying content itself (e.g., hear audioor see images, as examples) is referred to, with respect to the user, as‘consuming’ content, ‘consumption’ of content, ‘consumable’ contentand/or similar terms. For one or more embodiments, an electronicdocument may comprise a Web page coded in a markup language, such as,for example, HTML (hypertext markup language). In another embodiment, anelectronic document may comprise a portion or a region of a Web page.However, claimed subject matter is not intended to be limited in theserespects. Also, for one or more embodiments, an electronic documentand/or electronic entry may comprise a number of components. Componentsin one or more embodiments may comprise text, for example, in the formof physical signals and/or physical states (e.g., capable of beingphysically displayed). Also, for one or more embodiments, components maycomprise a graphical object, such as, for example, an image, such as adigital image, and/or sub-objects, such as attributes thereof, which,again, comprise physical signals and/or physical states (e.g., capableof being physically displayed). In an embodiment, content may comprise,for example, text, images, audio, video, and/or other types ofelectronic documents and/or portions thereof, for example.

Also as used herein, one or more parameters may be descriptive of acollection of signal samples, such as one or more electronic documents,and exist in the form of physical signals and/or physical states, suchas memory states. For example, one or more parameters, such as referringto an electronic document comprising an image, may include parameters,such as time of day at which an image was captured, latitude andlongitude of an image capture device, such as a camera, for example,etc. In another example, one or more parameters relevant to content,such as content comprising a technical article, may include one or moreauthors, for example. Claimed subject matter is intended to embracemeaningful, descriptive parameters in any format, so long as the one ormore parameters comprise physical signals and/or states, which mayinclude, as parameter examples, name of the collection of signals and/orstates (e.g., file identifier name), technique of creation of anelectronic document, purpose of an electronic document, time and date ofcreation of an electronic document, logical path of an electronicdocument (or portion thereof), encoding formats and/or standards usedfor encoding an electronic document, and so forth.

Signal packets and/or frames, also referred to as signal packettransmissions and/or signal frame transmissions, may be communicatedbetween nodes of a network, where a node may comprise one or morenetwork devices and/or one or more computing devices, for example. As anillustrative example, but without limitation, a node may comprise one ormore sites employing a local network address. Likewise, a device, suchas a network device and/or a computing device, may be associated withthat node. A signal packet and/or frame may, for example, becommunicated via a communication channel and/or a communication path,such as comprising a portion of the Internet and/or the Web, from a sitevia an access node coupled to the Internet. Likewise, a signal packetand/or frame may be forwarded via network nodes to a target site coupledto a local network, for example. A signal packet and/or framecommunicated via the Internet and/or the Web, for example, may be routedvia a path comprising one or more gateways, servers, etc. that may, forexample, route a signal packet and/or frame in accordance with a targetand/or destination address and availability of a network path of networknodes to the target and/or destination address. Although the Internetand/or the Web comprises a network of interoperable networks, not all ofthose interoperable networks are necessarily available and/or accessibleto the public.

In particular implementations, a network protocol for communicatingbetween devices may be characterized, at least in part, substantially inaccordance with a layered description, such as the so-called OpenSystems Interconnection (OSI) seven layer approach and/or description. Anetwork protocol refers to a set of signaling conventions, such as forcomputing and/or communications transmissions, as may, for example, takeplace between and/or among devices in a network, typically networkdevices; for example, devices that substantially comply with theprotocol and/or that are substantially compatible with the protocol. Inthis context, the term “between” and/or similar terms are understood toinclude “among” if appropriate for the particular usage and vice-versa.Likewise, in this context, the terms “compatible with”, “comply with”and/or similar terms are understood to include substantial complianceand/or substantial compatibility.

Typically, a network protocol, such as protocols characterizedsubstantially in accordance with the aforementioned OSI description, hasseveral layers. These layers may be referred to here as a network stack.Various types of transmissions, such as network transmissions, may occuracross various layers. A lowest level layer in a network stack, such asthe so-called physical layer, may characterize how symbols (e.g., bitsand/or bytes) are transmitted as one or more signals (and/or signalsamples) over a physical medium (e.g., twisted pair copper wire, coaxialcable, fiber optic cable, wireless air interface, combinations thereof,etc.). Progressing to higher-level layers in a network protocol stack,additional operations may be available by initiating networktransmissions that are compatible and/or compliant with a particularnetwork protocol at these higher-level layers. For example, higher-levellayers of a network protocol may, for example, affect devicepermissions, user permissions, etc.

A virtual private network (VPN) may enable a remote device to moresecurely (e.g., more privately) communicate via a local network. Arouter may allow network communications in the form of networktransmissions (e.g., signal packets and/or frames), for example, tooccur from a remote device to a VPN server on a local network. A remotedevice may be authenticated and a VPN server, for example, may create aspecial route between a local network and the remote device through anintervening router. However, a route may be generated and/or alsoregenerated if the remote device is power cycled, for example. Also, aVPN typically may affect a single remote device, for example, in somesituations.

A network may be very large, such as comprising thousands of nodes,millions of nodes, billions of nodes, or more, as examples. Asmentioned, with networks, such as the Internet and/or the Web, gainingpopularity, and with a vast multitude of content, such as pages, otherelectronic documents, other media content and/or applications(hereinafter ‘digital content’), becoming available to users, such asvia the World Wide Web (herein ‘Web’), it may be desirable to providemore efficient and/or more streamlined approaches to gather, organizeand/or display content, such as digital content, that may be desired byand/or useful to a user, for example. Internet-type business entities,such as Yahoo!, for example, may provide a wide range of content, suchas digital content, that may be made available to users, such as via theWeb. Internet-type business entities, such as Yahoo!, for example, mayprovide image content, such as digital image content, to clients, suchas client computing devices, for example. In some circumstances,challenges may be faced in providing image content, such as digitalimage content, to client computing devices, such as for display within aWeb page at a client computing device, for example.

FIG. 1 is a schematic diagram illustrating an example embodiment 100 ofa network computing and/or communications system. Example embodiment 100may comprise a server computing device 110, such as one or more that mayowned and/or operated by a Web page publisher 110, for example. Ofcourse, 110 is merely an illustrative example. Continuing, nonetheless,server 110 may provide one or more Web pages to a computing device, suchas client computing device 120, again, as an illustrative example. Asmentioned, a Web page may comprise (e.g. store) code, such as code thatmay be implemented in JavaScript, as one example, that may be executedby a computing device, such as 120, at least in part in response to thecomputing obtaining a Web page transmission (e.g., a transmission of aWeb page or a portion thereof) from a server, for example. In anembodiment, at least in part in response to executing a code segment,such as a Javascript code segment, a computing device, such as 120, maytransmit a request for an image, for example. As an illustration, anInternet-type business entity, such as a digital image provider, may,for example, own and/or operate a server, such as 130 in FIG. 1. Theremight be an associated service charge or it may simply be desirable tohave Web site and/or Web page user traffic, such as to enticeadvertisers. It is noted that a number of approaches are possible andclaimed subject matter is not intended to be limited in scope in thisrespect.

In an embodiment, an Internet-type business entity, such as a digitalimage provider, discussed above with respect to server 130, for example,may provide digital images to a computing device, such as 120, forexample, at least in part in response to receiving a request, such asfrom 120, in an example. Thus, in an embodiment, a digital image,provided by server 130, for example, may be displayed, such as via a Webpage transmitted to a computing device, such as 120. Also, in anembodiment, a computing device, such as 120, may provide one or morespecifications for a digital image, such as to 130, as part of a digitalimage request, for example.

A Web page transmitted from a server, such as 110, to a client computingdevice, such as 120, for example, may specify one or more parameters(e.g., specifications) for an image, such as a digital image. In anembodiment, a Web page may also comprise executable code, such as may beimplemented in Javascript, for example, that, if executed by a computingdevice, such as 120, may result in a computing device, such as 120,initiating transmissions of a request for a specified digital image, forexample, such as to a server, such as 130. Likewise, at least in part inresponse to receiving a request, such as for an image, a server, such as130, may transmit an appropriate image, such as a digital image, to acomputing device, such as 120, in an embodiment.

A request for an image, such as a digital image, submitted by acomputing device, such as 120, to a server, such as 130, may specify aparticular image, such as a digital image, and/or may specify one ormore parameters, from which server 130, for example, may select and/ortransmit an appropriate image, in an embodiment. Example parameters thatmay be specified may include, but are not limited to, subject matter,image format, size, and/or aspect ratio, in an embodiment.

As mentioned, in some circumstances, challenges may exist in providingimage content, such as digital image content, to client computingdevices, such as for display as part of a Web page being displayed, forexample. Challenges may include, for example, providing an image in anaspect ratio specified by a client computing device and/or suitable toview the image, as mentioned, for example, as part of a Web page, as anexample. A server computing device, such as 130, may store a pluralityof images in a storage area, such as an area designated and/or intendedfor image content in particular. A plurality of images, likewise, maycomprise any of a variety of aspect ratios, in an embodiment. As usedherein, the term “aspect ratio” and/or similar terms refer to aproportional relationship between an image's width and its height. Anaspect ratio may be expressed, for example, as signal samples havingvalues separated by a colon, such as 4:3, for example, although this isintended as a non-limiting illustrative example. Also, in an embodiment,a server, such as 130, may perform one or more processes, including, forexample, aspect ratio conversion and/or cropping, such as with respectto an image, in preparation for transmission of the image to a clientcomputing system, such as to 120, for example. In an embodiment,providing an image having a particular aspect ratio may compriseselecting an image, and/or specifying a cropping window, such as overthe image, that satisfies a particular aspect ratio, for example.

FIG. 2 is an illustration of an example embodiment 200 of a process forimage cropping, such as digital image cropping, according to anembodiment. Embodiments in accordance with claimed subject matter mayinclude all of blocks 210-220, more than blocks 210-220, or less thanblocks 210-220. Further, the order of blocks 210-220 is merely anexample order, and claimed subject matter is not intended to be limitedin scope in this respect.

In an embodiment, a composite saliency map may be generated for a regionof an image, as depicted at block 210. As used herein, the term“saliency map” and/or similar terms refer to content, such as digitalimage content, comprising two or more levels of conspicuity and/orsaliency with respect to human visual attention drawn, at leastinitially and/or momentarily, to one or more pixels, regions, and/orsub-regions of an image, such as a digital image, for example. Forexample, a saliency map may indicate one or more pixels, regions, and/orsub-regions more likely to attract, at least initially and/ormomentarily, attention of a human visually. Also, as used herein, theterm “composite saliency map” and/or similar terms refer to a saliencymap generated from a plurality of saliency maps. Likewise, a“pre-composite saliency map” and/or similar terms refer a saliency mapto be used as a component of a composite saliency map. As used herein,the terms “region” and “sub-region” are used interchangeably.Furthermore, the term “sub-region” refers to a region and/or to a partof the region. Likewise, as used herein, the terms “portion” and“sub-portion” are used interchangeably. Furthermore, the term“sub-portion” refers to a portion and/or to a part of the portion.

Additionally, for an embodiment, a cropping window may be determined fora region of an image, such as a digital imager, based, at least in part,on a composite saliency map, as depicted at block 220, for example. Asused herein, the term “cropping window” and/or similar terms refer to aportion of an image, such as a digital image, and/or of a region of animage, such as a digital image, from which a resulting image is to begenerated. As discussed more fully below, a cropping window may bedetermined based, at least in part, on a specified aspect ratio and/oron one or more saliency maps, in an embodiment. Further discussionrelated to cropping windows and/or saliency maps may be found below inconnection with one or more illustrative embodiments.

FIG. 3 is a schematic diagram illustrating another example embodiment300 of a process image cropping, such as digital image cropping,according to an embodiment. In an embodiment, an image, such as originalimage 310, may be retrieved from a storage area, such as, for example,an image content storage area, such as may be, for example, located at aserver, such as 130. In this context, the term “original” and/or similarterms refer to before beginning processing (e.g., before beginningprocessing by an embodiment substantially in accordance with claimedsubject matter) and/or as initially accessible. Thus, the term“original” and/or similar terms may, as an example, be employed withrespect to content, such as digital content, to be processed. Similarly,as additional examples, an original image 310 may comprise an originalformat, and/or may comprise an original aspect ratio. As used herein,therefore, the term “original format” may refer to a format of an imageprior to any format conversion that may occur as part of image retrievaland/or image cropping, in an embodiment. Also, as suggested, in anembodiment, the term “original aspect ratio” may refer to an aspectratio of an image prior to any aspect ratio conversion that may occur aspart of image retrieval and/or image cropping. Similarly, the term“original image” may refer to an image prior to any format conversion,aspect ratio conversion, image cropping, or any combination thereof,such as may occur as part of image retrieval and/or image cropping, inan embodiment, for example.

As depicted at block 320 of FIG. 3, original image 310 may undergoinitial processing, in an embodiment. For example, initial processingmay include, for example, a format conversion operation and/or a regionselection operation, although claimed subject matter is not limited inscope in this respect. Generally, in an embodiment, initial processing,such as depicted at block 320, may prepare an image, such as a digitalimage, for saliency mapping and/or cropping. For example, a saliency mapmay be generated, as depicted at block 330. At block 340, a croppingwindow for an image, such as a digital image, may be determined, in anembodiment. Also, for an embodiment, post-processing for an image, suchas a digital image, may be performed, as depicted at block 350, toproduce a resulting image, such as 360, which may comprise a digitalimage. In an embodiment, resulting digital image 360 may, for example,be specified, at least in part, by a cropping window, such as a croppingwindow determined at block 340, for example. Also, in an embodiment,post-processing may include, for example, converting one or morecropping window parameters back to an original image coordinate space,and/or may also include determining coordinates of a top-left corner ofa cropping window and/or a count of columns and/or rows of pixels of acropping window, in an embodiment. Of course, claimed subject matter isnot limited in scope in these respects.

FIG. 4 is an illustration depicting an example embodiment 400 of aprocess for selection of a region, such as region 414, of an exampleimage, such as example original image 410, in accordance with anembodiment. As mentioned above, initial image processing, such asdepicted at block 320 of FIG. 3, for example, may comprise regionselection, in an embodiment. As depicted in FIG. 4, original image 410,for this example, may comprise regions 412 and 414 that may concatenatedto form a composite image, such as image 410. As also mentioned above,initial processing of an image may comprise preparing the image forsaliency mapping and/or cropping. In some situations, such as withoriginal image 410, for example, it may be desirable to select a regionof an image in preparation for generating one or more saliency maps, inan embodiment, as described below using an illustration.

For example, for a situation wherein an image may have a width that issignificantly greater than its height, a cropping window for a specifiedaspect ratio may be smaller than may be desired. In some situations, ifan image has a width significantly greater than its height, the widthmay be a result of concatenating a plurality of smaller images, forexample. In an embodiment, an entirety of an original image at leastapproximately may be utilized to generate a cropping window at least inpart in response to a determination that a potential cropping windowgiven a particular aspect ratio, for example, has a width that exceeds athreshold, for example. In an embodiment, a threshold may comprise 60%of a width of an image, as an illustrative non-limiting example. Forexample, for a situation in which a potential cropping window for animage has a width that exceeds 60% of the width of the image,approximately the entire original image may be utilized to generate acropping window, in an embodiment. Also, in an embodiment, for asituation wherein a potential cropping window for an image has a widththat does not exceed a threshold, such as 60% of the width of anoriginal image, a region selection operation may be performed. It isalso noted that a threshold may comprise a programmable threshold, auser specified threshold, and/or an adaptive threshold, in alternativeembodiments, as examples.

In an embodiment, selecting a region, such as region 414, for example,from a larger image, such as original image 410, may comprise detectinga separating boundary, such as separating boundary 413, for example,that may indicate a border between two or more regions, such as regions412 and 414. In an embodiment, one or more edge-detection processes maybe utilized to detect one or more separating boundaries at least as partof a region selection process, although claimed subject matter is notlimited in scope to a particular edge detection approach.

Although example embodiments described herein describe regionscomprising contiguous portions of an image, other embodiments arepossible. For example, in an embodiment, a region may comprisenon-contiguous portions of an image, for example.

FIG. 5 is an illustration depicting an example embodiment 500 of aprocess for selecting a region, such as region 514, of an example image,such as digital image 510, for example. For the example depicted in FIG.5, image 510 may comprise a concatenation, for example, of a pluralityof smaller images, such as depicted as regions 512, 514, and 516,although claimed subject matter is not limited in scope in theserespects. To select a region, such as region 514, from an image, such asdigital image 510, for example, an edge detection process may beperformed to detect one or more separating boundaries, such asseparating boundaries 522 and 524, for example. For embodiment 500,depicted in FIG. 5, for example, image 520 may comprise output content,such as output digital content of an edge detection operation such asmay be performed with respect to image 710, for example. Additionally,in an embodiment, a chart, such as chart 530 shown in FIG. 5, may depictsignal samples having values comprising pixels determined to be locatedat and/or near an edge with respect to a plurality of columns of pixelsfor an image, such as digital image 510, for example. Thus, chart 530may indicate that a higher percentage of pixels may be located at and/ornear an edge at approximately pixel columns 500 and 1000, in anembodiment. For example, separating boundaries 522 and 524 may bedetermined to exist at approximately pixel columns 500 and 1000.Selection of a region of digital image 510 may be based, at least inpart, at least approximately, on determined positions of separatingboundaries 522 and/or 524, for example. Of course, claimed subjectmatter is not limited in scope in these respects.

As may be seen with respect to image 520 and chart 530, for example,pixel columns close to separating boundaries, such as separatingboundaries 522 and/or 524, may have higher values than pixel columnsfarther away from separating boundaries. Also, as may be seen withrespect to image 510 and image 530, separating boundaries may beseparated one from another by a greater amount than pixel columnspositioned in the area of a separating boundary. Therefore, in anembodiment, a threshold may be specified whereby if a second pixelcolumn having a relatively high value (e.g. one or more signal samplevalues) is located greater than a threshold amount away from a firstpixel column having a relatively high value (e.g., one or more signalsample values), the second pixel column may be considered to beassociated with a separating boundary. Also, in an embodiment, if thesecond pixel column having a relatively high value is located less thana threshold amount away from the first pixel column having a relativelyhigh value, the second pixel column may be considered to be associatedwith the same separating boundary as the first pixel column, as anexample.

FIG. 6 is an illustration depicting an additional example embodiment 600of a process for selection of a region of another example digital image,such as digital image 610, for example. For the example of FIG. 6, image610 may lack visible separating boundaries between individual regions.As described more fully below, a region, such as region 620, may beselected from an image, such as digital image 610, at least in part bydetermining one or more gaps having pixel columns of approximatelysimilar signal sample values, for example.

FIG. 7 is an illustration depicting an example embodiment 700 of aprocess for selecting a region, such as region 714, of an example image,such as digital image 710. For the example depicted in FIG. 7, image 710may comprise a concatenation, for example, of a plurality of smallerimages, such as depicted as regions 712, 714, and 716, although claimedsubject matter is not limited in scope in these respects. To select aregion, such as region 714, from an image, such as digital image 710, anedge detection process may be performed to detect one or more separatingboundaries. For example, for embodiment 700, image 720 may compriseoutput content, such as output digital content, of an edge detectionoperation performed with respect to image 710.

However, in situations where boundaries are not easily recognizable(e.g., apparent) between portions and/or regions of an image, forexample, it may prove more challenging to detect separating boundaries.For example, edge detection output image 720 does not appear to identifyboundaries between regions. Chart 730 shown in FIG. 7, for example,depicts signal sample values corresponding to pixels determined to notbe located at and/or near an edge across a plurality of columns ofpixels for image 710. Thus, for example, chart 730 in this non-limitingillustrative example indicates regions of image 710 that have higherpercentages, on a relative basis, of pixels not located at and/or nearan edge. See, for example, signal sample values for pixel columns inareas approximately surrounding pixel columns 500 and 1000. In anembodiment, areas approximately surrounding pixel columns 500 and 1000may be approximately bisected to form separating boundaries, such asseparating boundaries 722 and 724, for example. In an embodiment,selection of a region of image 710 may be based, at least in part, onareas of edge detection output image 720 determined to include pixelcolumns having at least approximately no edge pixels. Of course, claimedsubject matter is not limited in scope in these respects. Otherapproaches may, for example, be employed and are intended to be coveredby claimed subject matter.

As discussed above, FIGS. 4-7 illustrate example embodiments ofprocesses for selection of regions of images. In the discussion thatfollows, an example process for detecting separating boundaries in animage, such as a digital image, is described that may be utilized in oneor more embodiments. Although embodiments described herein may detectvertically separated regions in an image, other embodiments may detecthorizontally separated regions in an image, for example.

In an embodiment, an image, such as a digital image, may be representedas an array (e.g. matrix) of signal samples having values, such as anarray of signal samples image(i,j), which may comprise one or more pixelintensities in the form of one or more signal sample values, such aslocated at an i^(th) row and a j^(th) column of the array (e.g.,matrix). Likewise, in an embodiment, an edge map may comprise a matrix,in an embodiment, having binary digital signal sample values, whereinedge(i,j)=1 may signify that the i^(th) row and j^(th) column comprisesan edge of a particular digital image, and wherein edge(i,j)=0 signifiesthat the i^(th) row and j^(th) column does not comprise an edge of theparticular digital image, in an embodiment. Likewise, continuing, in anembodiment, cols may represent a count of columns in edge, and rows mayrepresent a count of rows in edge. Also, in an embodiment, sub-regionsmay be identified as a list of tuples of signal samples. For example, inan embodiment, starting and ending columns of a sub-region of an imagemay have a form, for example, comprising a tuple of signal sampleshaving values, wherein the first and second elements of a particulartuple, for example, may respectively comprise the starting and endingcolumns in a sub-region. An empty list of tuples may, in an embodiment,signify that no sub-region has been detected, for example.

Thus, example processes, including examples A1 and A2, for detecting aseparating boundary, may be implemented substantially in accordance withthe following pseudo-code:

Example Process A1: Detect Separating Boundaries  index ← [“index” maycomprise output signal samples from Example  Process A2, below]  $\left. {gap}\leftarrow\frac{cols}{10} \right.,\left. {curStart}\leftarrow 1 \right.,\left. {prevID}\leftarrow 1 \right.$ boundary ← empty list  for i = 2: length(index) do   i0 ← index(i − 1),i1 ← index(i)   if (i1 − i0 > gap)|(i = length(index)) then    id ←┌(index(i − 1) + index(prevID))/2┐    append (curStart, id) to boundary   prevID ← i, curStart ← 1  return boundary Example Process A2: DetectSeparating Boundary Indices  index ← empty list  $\left. {start}\leftarrow{{round}\left( \frac{cols}{10} \right)} \right.$ end ← cols − start  for i = [start, end] do   if i + 2 > cols thenbreak   l → edge(:, i) | edge(:, i + 1) | edge(:, i + 2)   $\left. {ratio}\leftarrow\frac{\sum\limits_{\forall_{i}}\; {l(i)}}{rows} \right.$  if ratio > 0.85 then append ratio to index  if index ! = empty thenreturn index  for i = [start, end] do   if i + 2 > cols then break   v ←image(1, i + 1), x ← image(:, i)   y ← image(:, i + 1), z ← image(:, i +2)   l ← (x = v)&(y = v)&(z = v)   $\left. {ratio}\leftarrow\frac{\sum\; {l(i)}}{rows} \right.$   ifratio > 1 then append ratio to index  return indexAlthough these example processes, including examples A1 and A2, referredto above, are described in details, claimed subject matter is notlimited in scope to example embodiments provided as illustrations, suchas the foregoing. Other embodiments in accordance with claimed subjectmatter may employ other techniques and/or processes to detect separatingboundaries and/or for region selection, for example.

Returning again to FIG. 3, in an embodiment, for example, followinginitial processing operations, such as, for example, a region selectionoperation, one or more saliency maps may be generated, such as depictedat block 330. As mentioned previously, in an embodiment, a plurality ofsaliency maps, such as pre-composite saliency maps, may be combined togenerate a composite saliency map. Likewise, in an embodiment, croppingwindow determination operations may be based on a composite saliencymap. Example types of pre-composite saliency maps (e.g., that may becombined) to produce a composite saliency map may include a Boolean-typesaliency map (also referred to as a BMS-type saliency map), a Face-typesaliency map, a Position-type saliency map, or any combination thereof.Of course, claimed subject matter is not limited to specific saliencymap types, such as the previous illustrative examples.

As mentioned above, a saliency map, such as a pre-composite saliencymap, may comprise a BMS-type saliency map. In an embodiment, a BMS-typesaliency map may model human eye-attention in images, for example. In anembodiment, a BMS-type saliency map may, for example, comprise digitalcontent capturing predictions and/or estimations of areas more likely todraw an observer's momentary conscious awareness and/or initialattention with respect to a scene depicted in an image, such as adigital image. Also, in an embodiment, one or more signal samplescomprising one or more pixel values for an example BMS-type saliencymap, in an embodiment, may, for example, comprise binary digital signalvalues, such as a “0” or a “1,” for a particular value. Thus, as anexample, in one or more areas of a digital image predicted and/orestimated to attract an observer's momentary conscious awareness and/orinitial attention, signal samples may have pixel values comprisingbinary digital signal values of “1” (e.g., areas of contiguous “1”s forexample). Similarly, in one or more areas of a digital image predictedand/or estimated to not attract an observer's momentary consciousawareness and/or initial attention, one or more signal samples may haveone or more pixel values comprising binary digital signal values of “0”(e.g., areas of contiguous “1”s for example). However, claimed subjectmatter is not limited in scope in these respects. For example, the roleof “0” and “1” may be reversed in an embodiment. Also, embodiments inaccordance with claimed subject matter are not limited to any particulartechnique and/or process for generating a saliency map, such as asaliency map to model human eye-attention in images.

In an embodiment, a saliency map to model human eye-attention in images,such as a BMS-type saliency map, may be characterized, for example, by aset of binary images that may be generated by substantially randomlythresholding one or more color channels of an image. Also, in anembodiment, a BMS-type saliency map may be generated at least in part byanalyzing a topological structure of Boolean maps based, at least inpart, on a Gestalt principle of figure-ground segregation, for example.Of course, claimed subject matter is not limited in scope in theserespects.

FIG. 8 is an illustration depicting an example embodiment of a face-typesaliency map, such as pre-composite saliency map embodiment 820, basedat least in part on an example image, such as digital image 810, forexample. In an embodiment, a face-type saliency map may comprise digitalcontent having one or more locations for an image predicted and/orestimated to be more likely to include one or more human faces. In anembodiment, for example, one or more bounding boxes may be determinedfor one or more detected faces. Likewise, a particular detected facebounding box may be fit with a two-dimensional (2D) Gaussiandistribution. Also, in an embodiment, 2D Gaussian distributions may becombined and/or weighted at least in part in accordance with aconfidence score for a particular bounding box. Of course, claimedsubject matter is not limited in scope to any specific technique and/orprocess for generating a saliency map, such as a face-type saliency map.

For an example embodiment of a process for generating a face-typesaliency map, a 2D Gaussian distribution

_(k)(μ_(k), Σ_(k)) may comprise a face-type saliency map for b_(k),∀k∈[1, n], given a list of n bounding boxes b₁, b₂, . . . b_(n). In anembodiment, for individual k, a mean vector μ_(k) and covariance matrixΣ_(k) may be specified substantially as follows

$\begin{matrix}{{\mu_{k} = \begin{pmatrix}{dx}_{k} \\{dy}_{k}\end{pmatrix}},{\Sigma_{k} = \begin{pmatrix}\frac{cw}{3} & 0 \\0 & \frac{cw}{3}\end{pmatrix}}} & (1)\end{matrix}$

wherein dx_(k) and dy_(k) may comprise signal samples having values ofcolumn and row indices, respectively, for a top left coordinate ofb_(k), and wherein cw_(k) and ch_(k) may comprise signal samples havingvalues for a width and height, respectively, of b_(k). In an embodimenta face-type saliency map may be generated substantially in accordancewith the following expressions:

$\begin{matrix}{{{F\left( {i,j} \right)} = \frac{G\left( {i,j} \right)}{D}},{\forall\left( {i,j} \right)}} & (2)\end{matrix}$

wherein

G(i,j)=Σ_(k=1) ^(n) w _(i)

_(k)(i,j)  (3)

D= _(∀(i,j)) ^(max)Σ_(k=1) ^(n)

_(k)(i,j)  (4)

and wherein D comprises one or more signal samples having a value from adistribution in which one or more bounding boxes (such as, in anembodiment, all bounding boxes), for an embodiment, may have aconfidence score of 1. Of course, claimed subject matter is not limitedin scope to example embodiments provided as illustrative examples, suchas the foregoing. Again, other embodiments in accordance with claimedsubject matter may utilize other techniques and/or processes forgenerating a saliency map, such as a face-type saliency map, forexample.

FIG. 9 is an illustration depicting an example embodiment of apre-composite saliency map, such as position-type saliency mapembodiment 910. In an embodiment, a position-type saliency map may begenerated based, at least in part, on an observation that middle and/ortop parts of an image are more likely to draw attention of anindividual, at least initially and/or momentarily. In an embodiment, adigital image, for example, may be fit with a 2D Gaussian distribution

(μ, Σ) substantially in accordance with the following expression:

$\begin{matrix}{{\mu = \begin{pmatrix}\left\lceil \frac{1 + {cols}}{2} \right\rceil \\\left\lceil \frac{rows}{3} \right\rceil\end{pmatrix}},{\Sigma = \begin{pmatrix}C_{1} & 0 \\0 & C_{2}\end{pmatrix}}} & (5)\end{matrix}$

Of course, claimed subject matter is not limited in scope to anyspecific technique and/or process for generating a saliency map, such asa position-type saliency map, including the foregoing illustrativeexample embodiment.

As discussed above, a plurality of pre-composite saliency maps may becombined to produce a composite saliency map. FIG. 10 is a schematicdiagram illustrating an example process 1000 for generating an examplecomposite saliency map, such as composite saliency map embodiment 1030,for an example image, such as digital image 1010. In an embodiment, oneor more BMS-type saliency maps, one or more face-type saliency maps, oneor more position-type saliency maps, or any combination thereof, may becombined to produce a composite saliency map. For example, in anembodiment, a BMS-type saliency map, such as embodiment 1022, aface-type saliency map, such as face-type saliency map embodiment 1024,and a position-type saliency map, such as position type saliency mapembodiment 1026, may be combined to generate a composite saliency map,such as composite saliency map embodiment 1030.

In an embodiment, a composite saliency map may be generatedsubstantially in accordance with the following expressions:

S(i,j)=α(i,j)B(i,j)+β(i,j)F(i,j)+γ(i,j)P(i,j)  (6a)

wherein expression (6a) may be subject to

α(i,j)≥0,β(i,j)≥0,γ(i,j)≥0  (6b)

α(i,j)+β(i,j)+γ(i,j)=1  (6c)

∀(i,j), wherein i∈[1,M] and j∈[1,N]  (6d)

Also, in an embodiment, tuple (α_(ij), β_(ij), γ_(ij)) may be employedto at least partially affect weights of various saliency map sourceswith respect to different positions, for example. In an embodiment, forexample, α_(ij) may be set to 0.4, β_(ij) may be set to 0.5, and/orγ_(ij) may be set to 0.1∀(i,j). Of course, claimed subject matter is notlimited in scope in these respects.

Referring again to FIG. 3, a cropping window for an image may bedetermined at least in part in connection with and/or in response togeneration of a saliency map, as depicted at blocks 330 and 340, in anembodiment. FIG. 11 is an illustration depicting an example embodiment1100 of a selection of a cropping window, such as cropping window 1110,for a region of an example digital image, such as region 1030. In anembodiment, a resulting image, such as digital image 1120, may bespecified at least in part in connection with a cropping window, such ascropping window 1110, for example, in an embodiment. Thus, for example,in an embodiment, a specified cropping window may be determined based,at least in part, on an input image and based, at least in part, on aspecified aspect ratio. Additional cropping windows may be specified,for example, as being proportional to a specified cropping window size,although claimed subject matter is not limited in scope in this respect.In an embodiment, a specified cropping window size may comprise anapproximately maximum possible cropping window size for a given imagewithout, for example, exceeding the given image (and/or region),although claimed subject matter is not limited in scope in this respect.

Also, in an embodiment, a ratio r between a cropping window W and aparticular sized cropping window W spec may be specified, for example,at least in part substantially in accordance with the followingexpression:

$\begin{matrix}{r = \frac{{rows}(W)}{{rows}\left( W_{spec} \right)}} & (7)\end{matrix}$

Also, in an embodiment, cropping window W may be parameterized as(i,j,r), for example. Given the above, in an embodiment, a search for acropping window may be performed substantially in accordance with thefollowing expression:

_({i,j,r}) ^(spec)Σ_((u,v)∈(i,j,r)) S(u,v),∀(i,j,r)  (8)

wherein i∈[1, rows(I)], j∈[1, cols(I)], r∈[0,1], and wherein (i,j,r) maycomprise a set of signal samples having values for pixel indices thatmay be specified, at least in part, by parameterization (i,j,r), in anembodiment. In an embodiment, one or more signal samples having anapproximately maximum sample value may be obtained by setting r=1, forexample. Of course, claimed subject matter is not limited in scope inthese respects.

Further, in an embodiment, an integral image technique may be utilizedat least in part to calculate a matrix summation of a cropping window.For example, in an embodiment, an amount of pixels of an image may berepresented by P. Computing an integral image may be performed at leastin part by performing cumulative summation of columns of a matrix andperforming cumulative summation over rows of a matrix. A running timemay be represented as O(P), in an embodiment. In an embodiment, becausea number of possible search positions may be upper bounded by P, anexample cropping winder search operation may be performed in O(P),amount of time. For an example process, such as example cropping windowsearch process “B” described below, an approximately maximum croppingwindow height and width may be provided as input, and a variable r maybe set to “1” and an aspect ratio may be specified. Further, in anembodiment, an integral image computed at line 1 of example process Bmay comprise a matrix with (rows(saliency)+1) rows and(cols(saliency)+1) columns. Additionally, in an embodiment,integral(i,j) may comprise a total matrix value summation from column 1to i−1 and from row 1 to j−1. Also, if i=1 and/or j=1, integral(i,j)=0.

For example process B below, as inputs: saliency may comprise a matrixwherein saliency(i,j) comprises a saliency value of an i^(th) row andj^(th) column; ch_(max) represents a number of rows in a maximumcropping window; cw_(max) represents a number of columns in a maximumcropping window; cols represents a number of columns in saliency; androws represents a number of rows in saliency. in a maximum croppingwindow. Also, for example process B, an output may comprise a tuple (dx,dy), wherein dx represents a column index of top left corner, andwherein dy represents a row index of top left corner. Thus, exampleprocesses, including example B, for cropping window search, may beimplemented substantially in accordance with the following pseudo-code:

Example Process B: Cropping Window Search integral ← compute integralimage for saliency sum_(max) ← −1, dx ← 0, dy ← 0 for y0 = 1 : (rows −ch_(max) + 1) do y1 = y0 + ch_(max) for x0 = 1 : (cols − cw_(max) + 1)do x1 ← x0 + cw_(max) A ← integral(y0, x0) B ← integral(y0, x1) C ←integral(y1, x0) D ← integral(y1, x1) cursum ← A + D − B − C ifsum_(current) > sum_(max) then sum_(max) ← sum_(current) dx = x0 dy = y0return (dx, dy)

Of course, process B, described above, for cropping window search ismerely an example process, and claimed subject matter is not limited inthese respects.

As mentioned previously, post-processing may be performed such as inconnection with a window cropping operation, in an embodiment. See, forexample, blocks 340 and 350 of FIG. 3, as discussed above. In anembodiment, a resulting image, such as digital image 1120, may bespecified, at least in part, by a cropping window, such as croppingwindow 1110, for example. Post-processing may include, for example,converting one or more cropping window parameterizations back to anoriginal image coordinate space, and/or may also include determiningcoordinates of a top-left corner of a cropping window and/or counts ofcolumns and rows of pixels of a cropping window, in an embodiment. Ofcourse, claimed subject matter is not limited in scope in theserespects.

For purposes of illustration, FIG. 12 is an illustration of anembodiment of a system 1200 that may be employed in a client-server typeinteraction, such as described infra, in connection with cropping adigital image, such as at a network device and/or a computing device,for example. In FIG. 12, client computing device 1202 (first device′ infigure) may interface with computing device 1204 (second device′ infigure), which may comprise features of a server computing device, forexample. Communications interface 1230, processor (e.g., processingunit) 1220, and memory 1222, which may comprise primary memory 1224 andsecondary memory 1226, may communicate by way of a communication bus,for example. In FIG. 12, client computing device 1202 may represent oneor more sources of analog, uncompressed digital, lossless compresseddigital, and/or lossy compressed digital formats for content of varioustypes, such as video, imaging, text, audio, etc. in the form physicalstates and/or signals, for example. Client computing device 1202 maycommunicate with computing device 1204 by way of a connection, such asan internet connection, via network 1208, for example. Althoughcomputing device 1204 of FIG. 1 shows the above-identified components,claimed subject matter is not limited to computing devices having onlythese components as other implementations may include alternativearrangements that may comprise additional components or fewercomponents, such as components that function differently while achievingsimilar results. Rather, examples are provided merely as illustrations.It is not intended that claimed subject matter to limited in scope toillustrative examples.

Processor 1220 may be representative of one or more circuits, such asdigital circuits, to perform at least a portion of a computing procedureand/or process. By way of example, but not limitation, processor 1220may comprise one or more processors, such as controllers,microprocessors, microcontrollers, application specific integratedcircuits, digital signal processors, programmable logic devices, fieldprogrammable gate arrays, the like, or any combination thereof. Inimplementations, processor 1220 may perform signal processing tomanipulate signals and/or states, to construct signals and/or states,etc., for example.

Memory 1222 may be representative of any storage mechanism. Memory 1222may comprise, for example, primary memory 1224 and secondary memory1226, additional memory circuits, mechanisms, or combinations thereofmay be used. Memory 1222 may comprise, for example, random accessmemory, read only memory, etc., such as in the form of one or morestorage devices and/or systems, such as, for example, a disk drive, anoptical disc drive, a tape drive, a solid-state memory drive, etc., justto name a few examples. Memory 1222 may be utilized to store a program.Memory 1222 may also comprise a memory controller for accessing computerreadable-medium 1240 that may carry and/or make accessible content,which may include code, and/or instructions, for example, executable byprocessor 1220 and/or some other unit, such as a controller and/orprocessor, capable of executing instructions, for example.

Under direction of processor 1220, memory, such as memory cells storingphysical states, representing, for example, a program, may be executedby processor 1220 and generated signals may be transmitted via theInternet, for example. Processor 1220 may also receive digitally-encodedsignals from client computing device 1202.

Network 1208 may comprise one or more network communication links,processes, services, applications and/or resources to support exchangingcommunication signals between a client computing device, such as 1202,and computing device 1206 (third device′ in figure), which may, forexample, comprise one or more servers (not shown). By way of example,but not limitation, network 1208 may comprise wireless and/or wiredcommunication links, telephone and/or telecommunications systems, Wi-Finetworks, Wi-MAX networks, the Internet, a local area network (LAN), awide area network (WAN), or any combinations thereof.

The term “computing device,” as used herein, refers to a system and/or adevice, such as a computing apparatus, that includes a capability toprocess (e.g., perform computations) and/or store content, such asmeasurements, text, images, video, audio, etc. in the form of signalsand/or states. Thus, a computing device, in this context, may comprisehardware, software, firmware, or any combination thereof (other thansoftware per se). Computing device 1204, as depicted in FIG. 12, ismerely one example, and claimed subject matter is not limited in scopeto this particular example. For one or more embodiments, a computingdevice may comprise any of a wide range of digital electronic devices,including, but not limited to, personal desktop and/or notebookcomputers, high-definition televisions, digital versatile disc (DVD)players and/or recorders, game consoles, satellite television receivers,cellular telephones, wearable devices, personal digital assistants,mobile audio and/or video playback and/or recording devices, or anycombination of the above. Further, unless specifically stated otherwise,a process as described herein, with reference to flow diagrams and/orotherwise, may also be executed and/or affected, in whole or in part, bya computing device.

Memory 1222 may store cookies relating to one or more users and may alsocomprise a computer-readable medium that may carry and/or makeaccessible content, including code and/or instructions, for example,executable by processor 1220 and/or some other unit, such as acontroller and/or processor, capable of executing instructions, forexample. A user may make use of an input device, such as a computermouse, stylus, track ball, keyboard, and/or any other similar devicecapable of receiving user actions and/or motions as input signals.Likewise, a user may make use of an output device, such as a display, aprinter, etc., and/or any other device capable of providing signalsand/or generating stimuli for a user, such as visual stimuli, audiostimuli and/or other similar stimuli.

Regarding aspects related to a communications and/or computing network,a wireless network may couple client devices with a network. A wirelessnetwork may employ stand-alone ad-hoc networks, mesh networks, WirelessLAN (WLAN) networks, cellular networks, and/or the like. A wirelessnetwork may further include a system of terminals, gateways, routers,and/or the like coupled by wireless radio links, and/or the like, whichmay move freely, randomly and/or organize themselves arbitrarily, suchthat network topology may change, at times even rapidly. A wirelessnetwork may further employ a plurality of network access technologies,including Long Term Evolution (LTE), WLAN, Wireless Router (WR) mesh,2nd, 3rd, or 4th generation (2G, 3G, or 4G) cellular technology and/orthe like. Network access technologies may enable wide area coverage fordevices, such as client devices with varying degrees of mobility, forexample.

A network may enable radio frequency and/or other wireless typecommunications via a wireless network access technology and/or airinterface, such as Global System for Mobile communication (GSM),Universal Mobile Telecommunications System (UMTS), General Packet RadioServices (GPRS), Enhanced Data GSM Environment (EDGE), 3GPP Long TermEvolution (LTE), LTE Advanced, Wideband Code Division Multiple Access(WCDMA), Bluetooth, ultra wideband (UWB), 802.11b/g/n, and/or the like.A wireless network may include virtually any type of now known and/or tobe developed wireless communication mechanism by which signals may becommunicated between devices, between networks, within a network, and/orthe like.

Communications between a computing device and/or a network device and awireless network may be in accordance with known and/or to be developedcommunication network protocols including, for example, global systemfor mobile communications (GSM), enhanced data rate for GSM evolution(EDGE), 802.11b/g/n, and/or worldwide interoperability for microwaveaccess (WiMAX). A computing device and/or a networking device may alsohave a subscriber identity module (SIM) card, which, for example, maycomprise a detachable smart card that is able to store subscriptioncontent of a user, and/or is also able to store a contact list of theuser. A user may own the computing device and/or networking device ormay otherwise be a user, such as a primary user, for example. Acomputing device may be assigned an address by a wireless networkoperator, a wired network operator, and/or an Internet Service Provider(ISP). For example, an address may comprise a domestic or internationaltelephone number, an Internet Protocol (IP) address, and/or one or moreother identifiers. In other embodiments, a communication network may beembodied as a wired network, wireless network, or any combinationsthereof.

A device, such as a computing and/or networking device, may vary interms of capabilities and/or features. Claimed subject matter isintended to cover a wide range of potential variations. For example, adevice may include a numeric keypad and/or other display of limitedfunctionality, such as a monochrome liquid crystal display (LCD) fordisplaying text, for example. In contrast, however, as another example,a Web-enabled device may include a physical and/or a virtual keyboard,mass storage, one or more accelerometers, one or more gyroscopes, globalpositioning system (GPS) and/or other location-identifying typecapability, and/or a display with a higher degree of functionality, suchas a touch-sensitive color 2D or 3D display, for example.

A computing and/or network device may include and/or may execute avariety of now known and/or to be developed operating systems,derivatives and/or versions thereof, including personal computeroperating systems, such as a Windows, iOS, Linux, a mobile operatingsystem, such as iOS, Android, Windows Mobile, and/or the like. Acomputing device and/or network device may include and/or may execute avariety of possible applications, such as a client software applicationenabling communication with other devices, such as communicating one ormore messages, such as via protocols suitable for transmission of email,short message service (SMS), and/or multimedia message service (MMS),including via a network, such as a social network including, but notlimited to, Facebook, LinkedIn, Twitter, Flickr, and/or Google+, toprovide only a few examples. A computing and/or network device may alsoinclude and/or execute a software application to communicate content,such as, for example, textual content, multimedia content, and/or thelike. A computing and/or network device may also include and/or executea software application to perform a variety of possible tasks, such asbrowsing, searching, playing various forms of content, including locallystored and/or streamed video, and/or games such as, but not limited to,fantasy sports leagues. The foregoing is provided merely to illustratethat claimed subject matter is intended to include a wide range ofpossible features and/or capabilities.

A network may also be extended to another device communicating as partof another network, such as via a virtual private network (VPN). Tosupport a VPN, broadcast domain signal transmissions may be forwarded tothe VPN device via another network. For example, a software tunnel maybe created between a logical broadcast domain, and a VPN device.Tunneled traffic may, or may not be encrypted, and a tunneling protocolmay be substantially compliant with and/or substantially compatible withany now known and/or to be developed versions of any of the followingprotocols: IPSec, Transport Layer Security, Datagram Transport LayerSecurity, Microsoft Point-to-Point Encryption, Microsoft's Secure SocketTunneling Protocol, Multipath Virtual Private Network, Secure Shell VPN,another existing protocol, and/or another protocol that may bedeveloped.

A network may communicate via signal packets and/or frames, such as in anetwork of participating digital communications. A broadcast domain maybe compliant and/or compatible with, but is not limited to, now knownand/or to be developed versions of any of the following network protocolstacks: ARCNET, AppleTalk, ATM, Bluetooth, DECnet, Ethernet, FDDI, FrameRelay, HIPPI, IEEE 1394, IEEE 802.11, IEEE-488, Internet Protocol Suite,IPX, Myrinet, OSI Protocol Suite, QsNet, RS-232, SPX, System NetworkArchitecture, Token Ring, USB, and/or X.25. A broadcast domain mayemploy, for example, TCP/IP, UDP, DECnet, NetBEUI, IPX, Appletalk,other, and/or the like. Versions of the Internet Protocol (IP) mayinclude IPv4, IPv6, other, and/or the like.

Algorithmic descriptions and/or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processingand/or related arts to convey the substance of their work to othersskilled in the art. An algorithm is here, and generally, is consideredto be a self-consistent sequence of operations and/or similar signalprocessing leading to a desired result. In this context, operationsand/or processing involve physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical and/or magnetic signals and/or states capable of beingstored, transferred, combined, compared, processed or otherwisemanipulated as electronic signals and/or states representing variousforms of content, such as signal measurements, text, images, video,audio, etc. It has proven convenient at times, principally for reasonsof common usage, to refer to such physical signals and/or physicalstates as bits, values, elements, symbols, characters, terms, numbers,numerals, measurements, content and/or the like. It should beunderstood, however, that all of these and/or similar terms are to beassociated with appropriate physical quantities and are merelyconvenient labels. Unless specifically stated otherwise, as apparentfrom the preceding discussion, it is appreciated that throughout thisspecification discussions utilizing terms such as “processing,”“computing,” “calculating,” “determining”, “establishing”, “obtaining”,“identifying”, “selecting”, “generating”, and/or the like may refer toactions and/or processes of a specific apparatus, such as a specialpurpose computer and/or a similar special purpose computing and/ornetwork device. In the context of this specification, therefore, aspecial purpose computer and/or a similar special purpose computingand/or network device is capable of processing, manipulating and/ortransforming signals and/or states, typically represented as physicalelectronic and/or magnetic quantities within memories, registers, and/orother storage devices, transmission devices, and/or display devices ofthe special purpose computer and/or similar special purpose computingand/or network device. In the context of this particular patentapplication, as mentioned, the term “specific apparatus” may include ageneral purpose computing and/or network device, such as a generalpurpose computer, once it is programmed to perform particular functionspursuant to instructions from program software.

In some circumstances, operation of a memory device, such as a change instate from a binary one to a binary zero or vice-versa, for example, maycomprise a transformation, such as a physical transformation. Withparticular types of memory devices, such a physical transformation maycomprise a physical transformation of an article to a different state orthing. For example, but without limitation, for some types of memorydevices, a change in state may involve an accumulation and/or storage ofcharge or a release of stored charge. Likewise, in other memory devices,a change of state may comprise a physical change, such as atransformation in magnetic orientation and/or a physical change and/ortransformation in molecular structure, such as from crystalline toamorphous or vice-versa. In still other memory devices, a change inphysical state may involve quantum mechanical phenomena, such as,superposition, entanglement, and/or the like, which may involve quantumbits (qubits), for example. The foregoing is not intended to be anexhaustive list of all examples in which a change in state form a binaryone to a binary zero or vice-versa in a memory device may comprise atransformation, such as a physical transformation. Rather, the foregoingis intended as illustrative examples.

In the preceding description, various aspects of claimed subject matterhave been described. For purposes of explanation, specifics, such asamounts, systems and/or configurations, as examples, were set forth. Inother instances, well-known features were omitted and/or simplified soas not to obscure claimed subject matter. While certain features havebeen illustrated and/or described herein, many modifications,substitutions, changes and/or equivalents will now occur to thoseskilled in the art. It is, therefore, to be understood that the appendedclaims are intended to cover all modifications and/or changes as fallwithin claimed subject matter.

1. A method, comprising; obtaining, for a region of an image, aplurality of pre-composite saliency maps, each of the pre-compositesaliency maps corresponding to one of a plurality of saliency map types,at least one of the pre-composite saliency maps electronically modelinghuman eye-attention for the region of the image; generating, for theregion of the image, a composite saliency map from the plurality ofpre-composite saliency maps; and determining, for the region of theimage, a cropping window based, at least in part, on the compositesaliency map.
 2. The method of claim 1, the pre-composite saliency mapscomprising at least one of a face-type saliency map, a position-typesaliency map, or a boolean-type saliency map.
 3. The method of claim 1,wherein generating the composite saliency map comprises: applying, foreach of the pre-composite saliency maps, a different one of a pluralityof weights.
 4. The method of claim 1, further comprising: generating thepre-composite saliency maps.
 5. The method of claim 1, furthercomprising: detecting one or more separating boundaries in the image;and determining the region based, at least in part, on the one or moredetected separating boundaries.
 6. The method of claim 1, the region ofthe image comprising a contiguous portion of the image.
 7. The method ofclaim 1, the region of the image comprising non-contiguous portions ofthe image.
 8. An apparatus, comprising: a processor; and a memory, atleast one of the processor or the memory being configured to: obtain,for a region of an image, a plurality of pre-composite saliency maps,each of the pre-composite saliency maps corresponding to one of aplurality of saliency map types, at least one of the pre-compositesaliency maps electronically modeling human eye-attention for the regionof the image; generate, for the region of the image, a compositesaliency map from the plurality of pre-composite saliency maps; anddetermine, for the region of the image, a cropping window based, atleast in part, on the composite saliency map.
 9. The apparatus of claim8, at least one of the processor or the memory being further configuredto: generate the composite saliency map by applying, for each of thepre-composite saliency maps, a different one of a plurality of weights.10. The apparatus of claim 8, at least one of the processor or thememory being further configured to: generate the pre-composite saliencymaps.
 11. The apparatus of claim 8, at least one of the processor or thememory being further configured to: detect one or more separatingboundaries in the image; and determine the region based, at least inpart, on the one or more detected separating boundaries.
 12. Theapparatus of claim 8, the pre-composite saliency maps comprising atleast one of a face-type saliency map, a position-type saliency map, ora boolean-type saliency map.
 13. The apparatus of claim 8, the region ofthe image comprising a contiguous portion of the image.
 14. Theapparatus of claim 8, the region of the image comprising non-contiguousportions of the image.
 15. A non-transitory computer-readable storagemedium storing thereon computer-readable instructions configured to:obtain, for a region of an image, a plurality of pre-composite saliencymaps, each of the pre-composite saliency maps corresponding to one of aplurality of saliency map types, at least one of the pre-compositesaliency maps electronically modeling human eye-attention for the regionof the image; generate, for the region of the image, a compositesaliency map from the plurality of pre-composite saliency maps; anddetermine, for the region of the image, a cropping window based, atleast in part, on the composite saliency map.
 16. The non-transitorycomputer-readable storage medium as recited in claim 15, thecomputer-readable instructions being further configured to: generate thecomposite saliency map by applying, for each of the pre-compositesaliency maps, a different one of a plurality of weights.
 17. Thenon-transitory computer-readable storage medium as recited in claim 15,the computer-readable instructions being further configured to: generatethe pre-composite saliency maps.
 18. The non-transitorycomputer-readable storage medium as recited in claim 15, thecomputer-readable instructions being further configured to: detect oneor more separating boundaries in the image; and determine the regionbased, at least in part, on the one or more detected separatingboundaries.
 19. The non-transitory computer-readable storage medium asrecited in claim 15, the pre-composite saliency maps comprising at leastone of a face-type saliency map, a position-type saliency map, or aboolean-type saliency map.
 20. The non-transitory computer-readablestorage medium as recited in claim 15, the region of the imagecomprising a contiguous portion of the image.